Ride-sourcing platforms enable an on-demand shared transport service by solving decision problems often related to customer matching, pricing and vehicle routing. These problems have been frequently represented using aggregated mathematical models and solved via algorithmic approaches designed by researchers. The increasing complexity of ride-sourcing environments compromises the accuracy of aggregated methods. It, therefore, signals the need for alternative practices such as agent-based models which capture the level of complex dynamics in ride-sourcing systems. The use of these agent-based models to simulate ride-sourcing fleets has been a focal point of many studies; however, this occurred in the absence of a prescribed approach on how to build the models to mimic fleet operations realistically. To bridge this research gap, we provide a framework for building bespoke agent-based models for ride-sourcing fleets, derived from the fundamentals of agent-based modelling theory. We also introduce a model building sequence of the different modules necessary to structure a simulator based on our framework. To showcase the strength of our framework, we use it to tackle the highly non-linear problem of minimum fleet size estimation for autonomous ride-sourcing fleets. We do so by investigating the relationship of system parameters based on queuing theory principles and by deriving and validating a novel model for pickup wait times. By modelling the ride-sourcing fleet function in the urban areas of Manhattan, San Francisco, Paris and Barcelona, we find that ride-sourcing fleets operate queues with zero assignment times above the critical fleet size. We also show that pickup wait times have a pivotal role in the estimation of the minimum fleet size in ride-sourcing operations, with agent-based modelling to be a more reliable route for their identification given the system parameters.

中文翻译：

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使用基于代理的新型建模框架来确定关键的机队规模，用于自主驾驶

乘车采购平台通过解决通常与客户匹配，定价和车辆路线相关的决策问题，实现按需共享运输服务。这些问题经常使用汇总的数学模型来表示，并通过研究人员设计的算法解决。乘车出行环境的日益复杂性损害了汇总方法的准确性。因此，它暗示了对替代实践的需求，例如基于代理的模型，该模型捕获了乘车采购系统中复杂动态的水平。使用这些基于代理的模型来模拟乘车来源车队一直是许多研究的重点。但是，这是在缺少规定的方法来建立模型以实际模拟车队运营的情况下发生的。为了弥合这一研究差距，我们提供了一个框架，可从基于代理的建模理论的基础上为骑行采购车队构建定制的基于代理的模型。我们还介绍了根据我们的框架构建模拟器所需的不同模块的模型构建序列。为了展示我们框架的优势，我们使用它来解决高度非线性的最小化自动驾驶采购车队规模估计的问题。为此，我们根据排队论原理研究系统参数之间的关系，并推导并验证了新型的接机等待时间模型。通过对曼哈顿，旧金山，巴黎和巴塞罗那市区的乘车采购车队功能进行建模，我们发现，乘车采购车队运行的队列比关键车队规模大零分配时间。